blackL-Edges.
L-Edges such as HED [1]}, CATS [2]}, and DexiNed [3]} enjoy good property to decouple structures and textures.
So it is interesting to understand which type of L-Edges is in good favor of the inpainting task.
As in Fig. REF (h)-(j), we give some observations: HED edges are more indecisive near boundaries; CATS edges can get clearer results, but lose some continuous information; DexiNed edges can extract very dense L-Edges greatly benefited by learning from fine-grained annotations.
However, these L-Edges also suffer from corrupted images, as the example of CATS edges in Fig. REF (d).
To this end, we make TSR work for both edge detection and recovery trained with BCE on masked regions. This strategy can be well generalized to masked images without information leakage as in Fig. REF (e).
And, only one forward pass is needed to achieve binary L-Edge priors \(\mathbf {\tilde{P}}_{le}\)  from TSR.
